Three-Dimensional Diffusion Model in Sports Dance Video Human Skeleton Detection and Extraction

The research in this paper mainly includes as follows: for the principle of action recognition based on the 3D diffusion model convolutional neural network, the whole detection process is carried out from fine to coarse using a bottom-up approach; for the human skeleton detection accuracy, a multibranch multistage cascaded CNN structure is proposed, and this network structure enables the model to learn the relationship between the joints of the human body from the original image and effectively predict the occluded parts, allowing simultaneous prediction of skeleton point positions and skeleton point association information on the one hand, and refinement of the detection results in an iterative manner on the other. For the combination problem of discrete skeleton points, it is proposed to take the limb parts formed between skeleton points as information carriers, construct the skeleton point association information model using vector field, and consider it as a feature, to obtain the relationship between different skeleton points by using the detection method. It is pointed out that the reorganization problem of discrete skeleton points in multiperson scenes is an NP-Hard problem, which can be simplified by decomposing it into a set of subproblems of bipartite graph matching, thus proposing a matching algorithm for discrete skeleton points and optimizing it for the skeleton dislocation and algorithm problems of human occlusion. Compared with traditional two-dimensional images, audio, video, and other multimedia data, the 3D diffusion model data describe the 3D geometric morphological information of the target scene and are not affected by lighting changes, rotation, and scale transformation of the target and thus can describe the realistic scene more comprehensively and realistically. With the continuous updating of diffusion model acquisition equipment, the rapid development of 3D reconstruction technology, and the continuous enhancement of computing power, the research on the application of 3D diffusion model in the detection and extraction of a human skeleton in sports dance videos has become a hot direction in the field of computer vision and computer graphics. Among them, the feature detection description and model alignment of 3D nonrigid models are a fundamental problem with very important research value and significance and challenging at the same time, which has received wide attention from the academic community.

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A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware

Journal of Neurosurgery Spine ◽

10.3171/2019.6.spine19463 ◽

2019 ◽

Vol 31 (6) ◽

pp. 844-850 ◽

Cited By ~ 1

Author(s):

Kevin T. Huang ◽

Michael A. Silva ◽

Alfred P. See ◽

Kyle C. Wu ◽

Troy Gallerani ◽

...

Keyword(s):

Computer Vision ◽

Feature Detection ◽

High Accuracy ◽

Detection Accuracy ◽

Data Sets ◽

Visual Words ◽

Fusion Systems ◽

Kaze Feature ◽

Applications Of Machine Learning ◽

Cervical Plating

OBJECTIVERecent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision.METHODSPatient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors’ institution over a 10-year period (2008–2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations.RESULTSA total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance.CONCLUSIONSA computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.

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Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽

10.3390/s21124237 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4237

Author(s):

Hoon Ko ◽

Kwangcheol Rim ◽

Isabel Praça

Keyword(s):

Anomaly Detection ◽

Real Time ◽

Feature Detection ◽

Trading System ◽

Judgment Accuracy ◽

Energy Trading ◽

Real Time Processing ◽

Detection Model ◽

Time Required ◽

The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

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Design of bio-inspired binocular UAV detection system based on improved STC algorithm of scale transformation and occlusion detection

International Journal of Micro Air Vehicles ◽

10.1177/17568293211004846 ◽

2021 ◽

Vol 13 ◽

pp. 175682932110048

Author(s):

Huajun Song ◽

Yanqi Wu ◽

Guangbing Zhou

Keyword(s):

Detection System ◽

Rapid Development ◽

Reference Value ◽

Scale Transformation ◽

Hardware System ◽

Time Requirements ◽

Aerial Vehicle ◽

Spatio Temporal ◽

Small Targets ◽

Vision Detection

With the rapid development of drones, many problems have arisen, such as invasion of privacy and endangering security. Inspired by biology, in order to achieve effective detection and robust tracking of small targets such as unmanned aerial vehicles, a binocular vision detection system is designed. The system is composed of long focus and wide-angle dual cameras, servo pan tilt, and dual processors for detecting and identifying targets. In view of the shortcomings of spatio-temporal context target tracking algorithm that cannot adapt to scale transformation and easy to track failure in complex scenes, the scale filter and loss criterion are introduced to make an improvement. Qualitative and quantitative experiments show that the designed system can adapt to the scale changes and partial occlusion conditions in the detection, and meets the real-time requirements. The hardware system and algorithm both have reference value for the application of anti-unmanned aerial vehicle systems.

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MTI-YOLO: A Light-Weight and Real-Time Deep Neural Network for Insulator Detection in Complex Aerial Images

Energies ◽

10.3390/en14051426 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1426

Author(s):

Chuanyang Liu ◽

Yiquan Wu ◽

Jingjing Liu ◽

Jiaming Han

Keyword(s):

Feature Detection ◽

Feature Fusion ◽

Memory Storage ◽

Aerial Images ◽

Detection Accuracy ◽

Composite Insulator ◽

Running Time ◽

Scale Feature ◽

Multi Scale ◽

Good Trade

Insulator detection is an essential task for the safety and reliable operation of intelligent grids. Owing to insulator images including various background interferences, most traditional image-processing methods cannot achieve good performance. Some You Only Look Once (YOLO) networks are employed to meet the requirements of actual applications for insulator detection. To achieve a good trade-off among accuracy, running time, and memory storage, this work proposes the modified YOLO-tiny for insulator (MTI-YOLO) network for insulator detection in complex aerial images. First of all, composite insulator images are collected in common scenes and the “CCIN_detection” (Chinese Composite INsulator) dataset is constructed. Secondly, to improve the detection accuracy of different sizes of insulator, multi-scale feature detection headers, a structure of multi-scale feature fusion, and the spatial pyramid pooling (SPP) model are adopted to the MTI-YOLO network. Finally, the proposed MTI-YOLO network and the compared networks are trained and tested on the “CCIN_detection” dataset. The average precision (AP) of our proposed network is 17% and 9% higher than YOLO-tiny and YOLO-v2. Compared with YOLO-tiny and YOLO-v2, the running time of the proposed network is slightly higher. Furthermore, the memory usage of the proposed network is 25.6% and 38.9% lower than YOLO-v2 and YOLO-v3, respectively. Experimental results and analysis validate that the proposed network achieves good performance in both complex backgrounds and bright illumination conditions.

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Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit

Applied Sciences ◽

10.3390/app11136006 ◽

2021 ◽

Vol 11 (13) ◽

pp. 6006

Author(s):

Huy Le ◽

Minh Nguyen ◽

Wei Qi Yan ◽

Hoa Nguyen

Keyword(s):

Machine Learning ◽

Augmented Reality ◽

Object Detection ◽

Feature Detection ◽

Detection Methods ◽

Detection Accuracy ◽

Data Annotation ◽

Machine Learning Model ◽

Potential Benefits ◽

Feature Detection And Tracking

Augmented reality is one of the fastest growing fields, receiving increased funding for the last few years as people realise the potential benefits of rendering virtual information in the real world. Most of today’s augmented reality marker-based applications use local feature detection and tracking techniques. The disadvantage of applying these techniques is that the markers must be modified to match the unique classified algorithms or they suffer from low detection accuracy. Machine learning is an ideal solution to overcome the current drawbacks of image processing in augmented reality applications. However, traditional data annotation requires extensive time and labour, as it is usually done manually. This study incorporates machine learning to detect and track augmented reality marker targets in an application using deep neural networks. We firstly implement the auto-generated dataset tool, which is used for the machine learning dataset preparation. The final iOS prototype application incorporates object detection, object tracking and augmented reality. The machine learning model is trained to recognise the differences between targets using one of YOLO’s most well-known object detection methods. The final product makes use of a valuable toolkit for developing augmented reality applications called ARKit.

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Research on the Impact of Information Security Certification and Concealment onFinancial Performance

Journal of Global Information Management ◽

10.4018/jgim.20220801oa04 ◽

2022 ◽

Vol 30 (3) ◽

pp. 0-0

Keyword(s):

Information Security ◽

Financial Performance ◽

Positive Impact ◽

Rapid Development ◽

International Standards ◽

Corporate Decisions ◽

Using Data ◽

The Impact ◽

The Relationship ◽

Iso 27001

With the rapid development of information technology, information security has been gaining attention. The International Organization for Standardization (ISO) has issued international standards and technical reports related to information security, which are gradually being adopted by enterprises. This study analyzes the relationship between information security certification (ISO 27001) and corporate financial performance using data from Chinese publicly listed companies. The study focusses on the impact of corporate decisions such as whether to obtain certification, how long to hold certification, and whether to publicize information regarding certification. The results show that there is a positive correlation between ISO 27001 and financial performance. Moreover, the positive impact of ISO 27001 on financial performance gradually increases with time. In addition, choosing not to publicize ISO 27001 certification can negatively affect enterprise performance.

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Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources

Advances in Multimedia ◽

10.1155/2018/2058670 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16

Author(s):

Jun Long ◽

Lei Zhu ◽

Zhan Yang ◽

Chengyuan Zhang ◽

Xinpan Yuan

Keyword(s):

Social Networks ◽

Social Network ◽

Large Scale ◽

Multimedia Data ◽

Attribute Information ◽

Novel Method ◽

Relationship Network ◽

Heterogeneous Social Networks ◽

The Relationship ◽

Over Time

Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.

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JRL-YOLO: A Novel Jump-Join Repetitious Learning Structure for Real-Time Dangerous Object Detection

Computational Intelligence and Neuroscience ◽

10.1155/2021/5536152 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Yiliang Zeng ◽

Lihao Zhang ◽

Jiahong Zhao ◽

Jinhui Lan ◽

Biao Li

Keyword(s):

Target Detection ◽

Rapid Development ◽

Experimental Tests ◽

Detection Accuracy ◽

The Public ◽

Percent Increase ◽

Spatial Pyramid Pooling ◽

Model Size ◽

Intelligent Security ◽

Security Incidents

Campus security incidents occur from time to time, which seriously affect the public security. In recent years, the rapid development of artificial intelligence has brought technical support for campus intelligent security. In order to quickly recognize and locate dangerous targets on campus, an improved YOLOv3-Tiny model is proposed for dangerous target detection. Since the biggest advantage of this model is that it can achieve higher precision with very fewer parameters than YOLOv3-Tiny, it is one of the Tinier-YOLO models. In this paper, the dangerous targets include dangerous objects and dangerous actions. The main contributions of this work include the following: firstly, the detection of dangerous objects and dangerous actions is integrated into one model, and the model can achieve higher accuracy with fewer parameters. Secondly, to solve the problem of insufficient YOLOv3-Tiny target detection, a jump-join repetitious learning (JRL) structure is proposed, combined with the spatial pyramid pooling (SPP), which serves as the new backbone network of YOLOv3-Tiny and can accelerate the speed of feature extraction while integrating features of different scales. Finally, the soft-NMS and DIoU-NMS algorithm are combined to effectively reduce the missing detection when two targets are too close. Experimental tests on self-made datasets of dangerous targets show that the average MAP value of the JRL-YOLO algorithm is 85.03%, which increases by 3.22 percent compared with YOLOv3-Tiny. On the VOC2007 dataset, the proposed method has a 9.29 percent increase in detection accuracy compared to that using YOLOv3-Tiny and a 2.38 percent increase compared to that employing YOLOv4-Tiny, respectively. These results all evidence the great improvement in detection accuracy brought by the proposed method. Moreover, when testing the dataset of dangerous targets, the model size of JRL-YOLO is 5.84 M, which is about one-fifth of the size of YOLOv3-Tiny (33.1 M) and one-third of the size of YOLOv4-Tiny (22.4 M), separately.

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Green innovation, government incentive and enterprise performance are explored from the perspective of game

E3S Web of Conferences ◽

10.1051/e3sconf/202127502006 ◽

2021 ◽

Vol 275 ◽

pp. 02006

Author(s):

Xiaohui Ren

Keyword(s):

Game Theory ◽

Rapid Development ◽

Enterprise Performance ◽

Green Innovation ◽

Green Development ◽

Pollution Problem ◽

Environmental Pollution Problem ◽

And Performance ◽

The Relationship

The rapid development of economy brings serious environmental pollution problem. Green innovation, as the connection point between government environmental regulation measures and sustainable green development of enterprises, has become one of the important choices for the transformation and development of enterprises. Based on the classic model of “prisoner’s dilemma” in game theory, this paper deeply analyzes the relationship between green innovation and performance. It is found that it is easy to get into trouble if only relying on the spontaneous green innovation within the enterprise. Applying appropriate pressure outside the enterprise can promote the change of green innovation and bring long-term benefits to the enterprise.

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The Party Building Leads the Innovative Exploration of Grassroots Governance in Rural Pastoral Areas under the Concept of Co-governance

Scientific and Social Research ◽

10.36922/ssr.v3i5.1232 ◽

2021 ◽

Vol 3 (5) ◽

pp. 125-131

Author(s):

Dengpeng Jing

Keyword(s):

Rural Areas ◽

Public Services ◽

Rapid Development ◽

Grassroots Organizations ◽

Party Organization ◽

Party Organizations ◽

Party Building ◽

New Challenges ◽

The Masses ◽

The Relationship

With the rapid development of society and economy, grassroots organizations in rural pastoral areas are an important part of party building, shouldering the mission of implementing party policies, and playing an important role in leading herdsmen to fight poverty and realize basic modernization in rural areas. The mission and responsibilities of grassroots party organizations in rural and pastoral areas are undergoing profound changes. Strengthening the construction of grassroots party organizations in rural and pastoral areas will help promote the relationship between the party and the masses, cadres and the masses in rural and pastoral areas, and promote the establishment of party organizations in rural and pastoral areas. At present, grassroots party building in rural pastoral areas is facing new challenges, such as insufficient party organization building, and unclear power boundaries between party organizations and villagers’ autonomous organizations. Only by accelerating the construction of infrastructure and public services in rural pastoral areas and doing a good job in the construction of rural grassroots party organizations can improve the level of party building in rural pastoral areas and promote the basic modernization of rural areas.

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